Adjunct Instructor: AI Cloud and DevOps

The Heinz College of Information Systems and Public Policy at Carnegie Mellon University seeks an adjunct instructor for AI Cloud and DevOps for students in the Master of Science in Artificial Intelligence Systems Management (AIM) program. We invite professionals with deep experience and demonstrated leadership in the field to apply.

This course focuses on the productionization and operation of AI systems in cloud environments, examining the cloud infrastructure and DevOps practices required to deploy, scale, monitor, and govern AI applications in real-world organizational settings. Students study how machine learning models, large language models (LLMs), data pipelines, and emerging AI agents move from experimentation to reliable, maintainable production systems on modern cloud platforms.

Adopting a systems and lifecycle perspective, the course integrates concepts from cloud computing, DevOps, MLOps, and LLMOps. Topics include: cloud-native AI architectures; containerization and orchestration; CI/CD for data and model workflows; managed AI services across major cloud providers (e.g., AWS, Azure, and Google Cloud); monitoring and observability; operational risk management; and cost-performance trade-offs. The course also emphasizes how responsible AI principles, such as safety, accountability, security, and compliance, are embedded into operational workflows, particularly for LLM-based systems.

Through applied labs, case studies, and design-oriented assignments, students should gain hands-on experience deploying and operating AI systems within real cloud environments, including LLM-powered services and agent-based pipelines.

Recognizing AI Cloud and DevOps is a broad and complex topic, the minimum goals of this course are to prepare students to explain how AI systems are operationalized in modern cloud environments, to design end-to-end architectures for production AI systems, to apply DevOps, MLOps, and LLMOps principles across the AI lifecycle, to deploy and operate AI systems using major cloud platforms, to evaluate operational risks in deployed AI systems, to monitor and manage AI systems post-deployment, to assess security, privacy, and governance considerations in AI operations, to analyze trade-offs among scalability, reliability, performance, and cost, and to communicate AI system design and operational decisions to a multitude of stakeholders.

The course is a core course for students in the AIM program in their third, and final semester, of the program. The instructor should assume that the students have some baseline knowledge of Machine learning, Large Language Models, Data Pipelines, and emerging AI Agents. The instructor should be a practitioner with direct experience in the field of deploying and operating AI systems within cloud environments. Recent experience in teaching is preferred.

The course is a half semester (i.e. 7 weeks) during either the summer semester (June 22 - July 31). Course times could be afternoons (two 80 minute class sessions per week) or evenings (one 170 minute class from 6:30-9:20 PM, inclusive of a break, per week), as preferred.

The course design should at minimum include relevant readings (textbook, research papers, news articles, etc.), in-class discussions, and appropriate evaluations of mastery of concepts for grading purposes (homework, quizzes/exams, etc.). Given the focus of Heinz College graduate programs, utilization of data, strategic thinking, and application of leadership skills are highly encouraged to be integrated into the course.

Qualifications

The instructor should be a practitioner with direct experience in the field of deploying and operating AI systems within cloud environments. Recent experience in teaching is preferred.